Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data
Abstract
Computational Drug Repositioning (CDR) is the knowledge discovery process of finding new indications for existing drugs leveraging heterogeneous drug-related data. Longitudinal observational data such as Electronic Health Records (EHRs) have become an emerging data source for CDR. To address the high-dimensional, irregular, subject and time-heterogeneous nature of EHRs, we propose Baseline Regularization (BR) and a variant that extend the one-way fixed effect model, which is a standard approach to analyze small-scale longitudinal data. For evaluation, we use the proposed methods to search for drugs that can lower Fasting Blood Glucose (FBG) level in the Marshfield Clinic EHR. Experimental results suggest that the proposed methods are capable of rediscovering drugs that can lower FBG level as well as identifying some potential blood sugar lowering drugs in the literature. PDF
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Text
Kuang et al. "Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data." International Joint Conference on Artificial Intelligence, 2016.Markdown
[Kuang et al. "Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data." International Joint Conference on Artificial Intelligence, 2016.](https://mlanthology.org/ijcai/2016/kuang2016ijcai-baseline/)BibTeX
@inproceedings{kuang2016ijcai-baseline,
title = {{Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data}},
author = {Kuang, Zhaobin and Thomson, James A. and Caldwell, Michael and Peissig, Peggy L. and Stewart, Ron M. and Page, David},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2016},
pages = {2521-2528},
url = {https://mlanthology.org/ijcai/2016/kuang2016ijcai-baseline/}
}